Forecasting systematic risk by Least Angel Regression, AdaBoost and Kernel Ridge Regression
Bibliographic record
Abstract
In according to importance of risk in financial decision and investment is one of issue that helps to investors is existing tools and appropriate models in order to predict systematic risk. Aim of this research was forecasting systematic risk of companies admitted at Tehran stock Exchange by Least Angel Regression (LARS), AdaBoost and Kernel Ridge Regression (KRR) and comparing ability of the algorithms in order to find the best methods of the test. In this study the financial data of (1159 observations) during 2005-2014. We used MATLAB software vision (R2013b). Results indicated that Kernel Ridge Regression (KRR) with 9.65% error (90.35% confidence) in comparison with Least Angel Regression (LARS) with 12.15% error (87.85% confidence) and AdaBoost with 28.91% error (71.09 confidence) has more ability for forecasting systematic risk. Moreover, ability of forecasting systematic risk in Least Angel Regression (LARS) is more than AdaBoost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".